Cookiee235 opened a new issue, #18423:
URL: https://github.com/apache/tvm/issues/18423
Running meta_schedule.tune_tir on a valid TIR module triggers a crash inside
RewriteFuseSplitParallelVectorize -> Parallel with a ScheduleError.
This seems to happen during initial population generation
(SampleInitPopulation) in the evolutionary search, indicating an internal
schedule transform failure
### Actual behavior
```
2025-11-07 15:56:26 [INFO] [task_scheduler.cc:189] TaskScheduler picks Task
#0: "main"
Traceback (most recent call last):
File
"/share_container/LLMFuzz/TirFuzz/bugs/10-24_20-21/topi.reverse_sequence_1_M0.py",
line 33, in <module>
database = ms.tir_integration.tune_tir(mod=tir_mod, target='llvm
--num-cores=32', work_dir='./tune_tmp', max_trials_global=1,
num_trials_per_iter=1)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/tvm-latest/python/tvm/meta_schedule/tir_integration.py",
line 146, in tune_tir
return tune_tasks(
^^^^^^^^^^^
File "/software/tvm-latest/python/tvm/meta_schedule/tune.py", line 122, in
tune_tasks
task_scheduler.tune(
File
"/software/tvm-latest/python/tvm/meta_schedule/task_scheduler/task_scheduler.py",
line 132, in tune
_ffi_api.TaskSchedulerTune( # type: ignore # pylint: disable=no-member
File "python/tvm_ffi/cython/function.pxi", line 758, in
core.Function.__call__
File "<unknown>", line 0, in
tvm::meta_schedule::GradientBasedNode::Tune(tvm::ffi::Array<tvm::meta_schedule::TuneContext,
void>, tvm::ffi::Array<tvm::FloatImm, void>, int, int, int,
tvm::meta_schedule::Builder, tvm::meta_schedule::Runner,
tvm::ffi::Array<tvm::meta_schedule::MeasureCallback, void>,
tvm::ffi::Optional<tvm::meta_schedule::Database, void>,
tvm::ffi::Optional<tvm::meta_schedule::CostModel, void>)
File "<unknown>", line 0, in
tvm::meta_schedule::TaskSchedulerNode::Tune(tvm::ffi::Array<tvm::meta_schedule::TuneContext,
void>, tvm::ffi::Array<tvm::FloatImm, void>, int, int, int,
tvm::meta_schedule::Builder, tvm::meta_schedule::Runner,
tvm::ffi::Array<tvm::meta_schedule::MeasureCallback, void>,
tvm::ffi::Optional<tvm::meta_schedule::Database, void>,
tvm::ffi::Optional<tvm::meta_schedule::CostModel, void>)
File "<unknown>", line 0, in
tvm::meta_schedule::EvolutionarySearchNode::GenerateMeasureCandidates()
File "<unknown>", line 0, in
tvm::meta_schedule::EvolutionarySearchNode::State::GenerateMeasureCandidates()
File "<unknown>", line 0, in
tvm::meta_schedule::EvolutionarySearchNode::State::SampleInitPopulation(int)
File "<unknown>", line 0, in tvm::support::parallel_for_dynamic(int, int,
int, std::function<void (int, int)> const&) [clone .cold]
File "<unknown>", line 0, in tvm::runtime::detail::LogFatal::~LogFatal()
[clone .constprop.0]
File "<unknown>", line 0, in
tvm::runtime::detail::LogFatal::Entry::Finalize()
RuntimeError: parallel_for_dynamic error with Traceback (most recent call
last):
File "<unknown>", line 0, in
tvm::meta_schedule::GradientBasedNode::Tune(tvm::ffi::Array<tvm::meta_schedule::TuneContext,
void>, tvm::ffi::Array<tvm::FloatImm, void>, int, int, int,
tvm::meta_schedule::Builder, tvm::meta_schedule::Runner,
tvm::ffi::Array<tvm::meta_schedule::MeasureCallback, void>,
tvm::ffi::Optional<tvm::meta_schedule::Database, void>,
tvm::ffi::Optional<tvm::meta_schedule::CostModel, void>)
File "<unknown>", line 0, in
tvm::meta_schedule::TaskSchedulerNode::Tune(tvm::ffi::Array<tvm::meta_schedule::TuneContext,
void>, tvm::ffi::Array<tvm::FloatImm, void>, int, int, int,
tvm::meta_schedule::Builder, tvm::meta_schedule::Runner,
tvm::ffi::Array<tvm::meta_schedule::MeasureCallback, void>,
tvm::ffi::Optional<tvm::meta_schedule::Database, void>,
tvm::ffi::Optional<tvm::meta_schedule::CostModel, void>)
File "<unknown>", line 0, in
tvm::meta_schedule::EvolutionarySearchNode::GenerateMeasureCandidates()
File "<unknown>", line 0, in
tvm::meta_schedule::EvolutionarySearchNode::State::GenerateMeasureCandidates()
File "<unknown>", line 0, in
tvm::meta_schedule::EvolutionarySearchNode::State::SampleInitPopulation(int)
File "<unknown>", line 0, in tvm::support::parallel_for_dynamic(int, int,
int, std::function<void (int, int)> const&)
File "<unknown>", line 0, in
tvm::meta_schedule::EvolutionarySearchNode::State::SampleInitPopulation(int)::{lambda(int,
int)#1}::operator()(int, int) const
File "<unknown>", line 0, in
tvm::meta_schedule::ThreadedTraceApply::Apply(tvm::IRModule const&,
tvm::tir::Trace const&, long*)
File "<unknown>", line 0, in
tvm::meta_schedule::RewriteParallelVectorizeUnrollNode::Apply(tvm::tir::Schedule
const&)
File "<unknown>", line 0, in
tvm::tir::RewriteFuseSplitParallelVectorize(tvm::tir::Schedule const&,
tvm::ffi::Array<tvm::tir::LoopRV, void>*, int)
File "<unknown>", line 0, in
tvm::tir::TracedScheduleNode::Parallel(tvm::tir::LoopRV const&)
File "/software/tvm-latest/src/tir/schedule/concrete_schedule.cc", line
630, in virtual void tvm::tir::ConcreteScheduleNode::Parallel(const
tvm::tir::LoopRV&)
ScheduleError: (not rendered)
```
### Environment
tvm-latest
### Steps to reproduce
```
import tvm
from tvm import te, topi, tir
from tvm import meta_schedule as ms
tir_str = """# from tvm.script import ir as I
# from tvm.script import tir as T
@I.ir_module
class Module:
@T.prim_func
def main(a: T.Buffer((5, 10, 8, 3), "int16"), seq_lengths:
T.Buffer((5,), "int64"), T_reverse_sequence: T.Buffer((5, 10, 8, 3), "int16")):
T.func_attr({"target": T.target({"keys": ["cpu"], "kind": "llvm",
"mtriple": "x86_64-unknown-linux-gnu", "tag": ""}), "tir.noalias": True})
# with T.block("root"):
for ax0, ax1, ax2, ax3 in T.grid(5, 10, 8, 3):
with T.block("T_reverse_sequence"):
v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1,
ax2, ax3])
T.reads(a[v_ax0, v_ax1, 0:8, v_ax3], seq_lengths[v_ax0])
T.writes(T_reverse_sequence[v_ax0, v_ax1, v_ax2, v_ax3])
T_reverse_sequence[v_ax0, v_ax1, v_ax2, v_ax3] = a[v_ax0,
v_ax1, T.if_then_else(seq_lengths[v_ax0] <= T.int64(1) or seq_lengths[v_ax0] <=
T.Cast("int64", v_ax2), T.Cast("int64", v_ax2), T.if_then_else(T.int64(8) <
seq_lengths[v_ax0], T.int64(7) - T.Cast("int64", v_ax2), seq_lengths[v_ax0] -
T.Cast("int64", v_ax2) - T.int64(1))), v_ax3]
for i, j, k in T.grid(5, 10, 8):
with T.block("additional_access"):
v_i, v_j, v_k = T.axis.remap("SSS", [i, j, k])
T.reads(T_reverse_sequence[v_i, v_j, v_k, 0:3])
T.writes(T_reverse_sequence[v_i, v_j, v_k, 0:3])
for l in range(3):
T_reverse_sequence[v_i, v_j, v_k, l] =
T_reverse_sequence[v_i, v_j, v_k, l] + T.int16(1)
"""
tir_mod = tvm.script.from_source(tir_str)
tir_mod.show()
database = ms.tir_integration.tune_tir(mod=tir_mod, target='llvm
--num-cores=32', work_dir='./tune_tmp', max_trials_global=1,
num_trials_per_iter=1)
```
### Triage
* needs-triage
* meta-tune
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